Leila Haddad Software Engineer
San Francisco, CA • leila.haddad@gmail.com • +1 415-555-0187
Profile Summary
- Software Engineer with 6 years of experience building consumer web and mobile products for local search and food delivery apps used by millions of people, focused on backend services, API design, and product feature delivery.
- Hands-on with languages (Python, Java, TypeScript), frameworks (FastAPI, Spring Boot, React), data stores (MySQL, PostgreSQL, Redis), APIs and messaging (REST, gRPC, Apache Kafka), cloud and containers (AWS, Docker, Kubernetes), and testing and CI (pytest, JUnit, GitHub Actions), on a solid base of data structures, concurrency, and distributed systems.
- Skilled in service decomposition, event-driven architecture, feature flags and A/B testing, and SQL query tuning, working from a design doc reviewed before every major build and a test pyramid that runs on every pull request to deliver features that launch on schedule and stay up after launch.
- Partners closely with product managers, designers, data scientists, and mobile engineers through weekly planning and a shared on-call rotation, from shaping a spec with product to watching the launch metrics after release.
- Coaches newer engineers on API design and writing testable code in code review and pairing sessions, leads the team’s biweekly architecture review, and keeps the team’s service ownership guide current.
Technical Skills
- Programming Languages:
- Python, Java, TypeScript, Go, SQL, Bash
- Frameworks:
- FastAPI, Spring Boot, React, Django, Node.js, Next.js
- Databases & Caching:
- MySQL, PostgreSQL, Redis, Amazon DynamoDB, Elasticsearch
- APIs & Messaging:
- REST, gRPC, GraphQL, Apache Kafka, Amazon SQS, OpenAPI
- Cloud & DevOps:
- AWS, Docker, Kubernetes, Terraform, GitHub Actions, Helm
- Testing Frameworks:
- pytest, JUnit, Jest, Playwright, Testcontainers, Pact
- Tools & Observability:
- Git, Linux, Datadog, Grafana, Sentry, GitHub Copilot, Jira
Education
Work Experience
- Shipped the online waitlist in React and TypeScript on a FastAPI backend (live queue position, SMS alerts, a host dashboard) behind feature flags, then ran an A/B test with product and design that raised weekly sign-ups per restaurant from 41 to 58.
- Wrote and defended the design doc that split the reservations monolith into 4 microservices on Kubernetes (availability, booking, notifications, partner sync) talking through Apache Kafka events with a transactional outbox, cutting release blocks between teams from 12 a month to 1.
- Refactored the Python search ranking module into typed, testable components (mypy strict mode, dependency injection, a shared scoring library) and held code drafted with GitHub Copilot to the same code review checklist, bringing bugs traced to it from 23 a quarter to 6.
- Built the REST booking API and an internal gRPC availability service in Java and Spring Boot for 14 partner apps, with API versioning, idempotency keys, per-partner rate limits and OpenAPI docs, now handling 1.2 million bookings a month.
- Redesigned the MySQL schema for business hours and menus (time zones, holiday overrides, menu versions) with zero-downtime migrations on 300 million rows using gh-ost, then added a Redis read cache on Amazon ElastiCache, cutting p95 query time from 380 ms to 45 ms.
- Rebuilt the reservations test suite as a fast test pyramid in pytest (unit tests, Testcontainers integration tests, Pact contract tests) running in Docker on every merge in GitHub Actions, which cut production regressions from 9 a quarter to 2.
- Traced slow search requests in Datadog APM, removed N+1 queries, added connection pooling and k6 load tests before each holiday peak, and took a weekly turn in the on-call rotation, bringing p99 latency at dinner time from 1.4 seconds to 420 ms.
- Mentored 4 engineers through pair programming and code reviews, led the biweekly design review and rewrote the onboarding guide around a starter ticket and a one-command Docker Compose setup, cutting a new hire’s time to first production deploy from 3 weeks to 4 days.
- Built the delivery time estimate service in Java and Spring Boot on AWS for 400,000 orders a day, adding live courier location and prep times to a machine learning model from data science, which cut late-delivery refunds by 18%.
- Moved order history reads from PostgreSQL to Amazon DynamoDB with a dual-write migration, a backfill job and shadow reads that compared both stores, cutting the order history page’s load time from 2.1 seconds to 600 ms.
- Wrote 140 integration tests for the checkout service with JUnit, Mockito and Testcontainers, covering promo codes, tips and split payments, and caught 31 bugs before release in the first 6 months.